Flexible information management strategies in machine learning and data mining

In recent times, a number of data rnining and machine learning techniques have been applied successfully to discover useful knowledge from data. Of the available techniques, rule induction and data clustering are two of the most useful and popular. Knowledge discovered from rule induction techniques in the form of If-Then rules is easy for users to understand and verify, and can be employed as classification or prediction models. Data clustering techniques are used to explore irregularities in the data distribution. Although rule induction and data clustering techniques are applied successfully in several applications, assumptions and constraints in their approaches have limited their capabilities. The main aim of this work is to develop flexible management strategies for these techniques to improve their performance. The first part of the thesis introduces a new covering algorithm, called Rule Extraction System with Adaptivity, which forms the whole rule set simultaneously instead of a single rule at a time. The rule set in the proposed algorithm is managed flexibly during the learning phase. Rules can be added to or omitted from the rule set depending on knowledge at the time. In addition, facilities to process continuous attributes directly and to prune the rule set automatically are implemented in the Rule Extraction System with Adaptivity algorithm The second part introduces improvements to the K-means algorithm in data clustering. Flexible management of clusters is applied during the learning process to help the algorithm to find the optimal solution. Another flexible management strategy is used to facilitate the processing of very large data sets. Finally, an effective method to determine the most suitable number of clusters for the K-means algorithm is proposed. The method has overcome all deficiencies of K-means

Vision dynamique pour la navigation d'un robot mobile

Les travaux présentés dans cette thèse concernent l’étude des fonctionnalités visuelles sur des scènes dynamiques et ses applications à la robotique mobile. Ces fonctionnalités visuelles traitent plus précisément du suivi visuel d’objets dans des séquences d’images. Quatre méthodes de suivi visuel ont été étudiées, dont trois ont été développées spécifiquement dans le cadre de cette thèse. Ces méthodes sont : (1) le suivi de contours par un snake, avec deux variantes permettant son application à des séquences d’images couleur ou la prise en compte de contraintes sur la forme de l’objet suivi, (2) le suivi de régions par différences de motifs, (3) le suivi de contours par corrélation 1D, et enfin (4) la méthode de suivi d’un ensemble de points, fondée sur la distance de Hausdorff, développée lors d’une thèse précédente. Ces méthodes ont été analysées pour différentes tâches relatives à la navigation d’un robot mobile; une comparaison dans différents contextes a été effectuée, donnant lieu à une caractérisation des cibles et des conditions pour lesquelles chaque méthode donne de bons résultats. Les résultats de cette analyse sont pris en compte dans un module de planification perceptuelle, qui détermine quels objets (amers plans) le robot doit suivre pour se guider le long d’une trajectoire. Afin de contrôler l’exécution d’un tel plan perceptuel, plusieurs protocoles de collaboration ou d’enchaînement entre méthodes de suivi visuel ont été proposés. Finalement, ces méthodes, ainsi qu’un module de contrôle d’une caméra active (site, azimut, zoom), ont été intégrées sur un robot. Trois expérimentations ont été effectuées: a) le suivi de route en milieu extérieur, b) le suivi de primitives pour la navigation visuelle en milieu intérieur, et c) le suivi d’amers plans pour la navigation fondée sur la localisation explicite du robot. ABSTRACT : The work presented on this thesis concerns the study of visual functionalities over dynamic scenes and their applications to mobile robotics. These visual functionalities consist on visual tracking of objects on image sequences. Four methods of visual tracking has been studied, from which tree of them has been developed specifically for the context of this thesis. These methods are: (1) snakes contours tracking, with two variants, the former, to be able to applying it to a sequence of color images and the latter to consider form constraints of the followed object, (2) the tracking of regions by templates differences, (3) contour tracking by 1D correlation, and (4) the tracking method of a set of points, based on Hausdorff distance, developed on a previous thesis. These methods have been analyzed for different tasks, relatives to mobile robot’s navigation. A comparison for different contexts has been done, given to a characterization of objects and conditions for which each method gives the best results. Results from this analysis has been take into account on a perceptual planification module, that determines which objects (plane landmarks) must be tracked by the robot, to drive it over a trajectory. In order to control the execution of perceptual plan, a lot of collaboration or chaining protocols have been proposed between methods. Finally, these methods and a control module of an active camera (pan, tilt, zoom), has been integrated on a robot. Three experiments have been done: a) road tracking over natural environments, b) primitives tracking for visual navigation over human environments and c) landmark tracking for navigation based on explicit localization of robo

Autonomous navigation and multi-sensorial real-time mocalization for a mobile robot

Doutoramento em Engenharia MecânicaO principio por detrás da proposta desta tese é a navegação de ambientes utilizando uma sequência de instruções condicionadas nas observações feitas pelo robô. Esta sequência é denominada como uma 'missão de navegação'. A interacção com um robô através de missões permitirá uma interface mais eficaz com humanos e a navegação de ambientes de maior escala e duma forma mais simplificada. No entanto, esta abordagem abre problemas novos no que diz respeito à forma como os dados sensoriais devem ser representados e utilizados. Neste trabalho representações binárias foram introduzidas para facilitar a integração dos dados multi-sensoriais, a dimensionalidade da qual foi reduzida através da utilização de Misturas de Distribuições de tipo Bernoulli. Foi também aplicada a técnica de cadeias de Markov ocultas (Hidden Markov Models), que contou com o desenvolvimento e a utilização dum modelo de cadeia de Markov original, esta que consegue explorar a informação contextual da sequência da missão. Uma aplicação que surgiu da aplicação do método de localização foi a criação de representações topologicas do ambiente sem ter que previamente recorrer à criação de mapas geométricos. Outras contribuições incluem a aplicação de métodos para a extracção de propriedades locais em imagens e o desenvolvimento de propriedades extraídas a partir de varrimentos dum medidor de distancia laser.This thesis evaluates the requisites for the specification of mobile robot 'Missions' for navigation within environments that are typically used by human beings. The principal idea behind the proposal of this thesis was to allow localization and navigation by providing a sequence of instructions, the execution of each instruction being conditional on the expected sensor data. This approach to navigation is expected to lead to new applications which will include the autonomous navigation of environments of very large scale. It is also expected to lead to a more intuitive interaction between mobile robots and humans. However, the concept of the navigation Mission opens up new problems namely in the way in which the sequence of instructions and the expected observations are to be represented. To solve this problem, binary features were used to integrate observations from multiple sensors, the dimensionality of which was reduced by modelling the binary data as a Finite Mixture Model comprised of Bernoulli distributions. Another original contribution was the modification of the Markov Chains used in Hidden Markov Models to enable the use of the sequential context in which the expected observations are specified in the navigation Mission. The localization method that was developed enabled the direct creation of a topological representation of an environment without recourse to an intermediate geometric map. Other contributions include developments that were made in the characterisation of images through the application of local features and of laser range scans through the creation of original features based on the scan contour and free-area properties

Information Sampling for Vision-based Robot Navigation

This paper proposes a statistical, non-feature based, attention mechanism for a mobile robot, termed Information Sampling. The selected data may be a single pixel or a number scattered throughout an image. After ranking this data, we choose only the most discriminating to build a topological representation of the environment, obtained via Principal Component Analysis (PCA). Advantageously, using this approach, our robot gains the ability to make effective use of its perceptual capabilities and limited computational resources. Real world experimental results verify that vision-based navigation is possible using only a small number of discriminating image pixels